SoroushVahidi/scidocs huggingface dataset:
Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning
(2017)

Cooling system plays a critical role in a modern data center (DC). Developing an optimal control policy for DC cooling system is a challenging task. The prevailing approaches often rely on approximating system models that are built upon the knowledge of mechanical cooling, electrical and thermal management, which is difficult to design and may lead to suboptimal or unstable performances. In this paper, we propose utilizing the large amount of monitoring data in DC to optimize the control policy. To do so, we cast the cooling control policy design into an energy cost minimization problem with temperature constraints, and tap it into the emerging deep reinforcement learning (DRL) framework. Specifically, we propose an end-toend cooling control algorithm (CCA) that is based on the actorcritic framework and an off-policy offline version of the deep deterministic policy gradient (DDPG) algorithm. In the proposed CCA, an evaluation network is trained to predict an energy cost counter penalized by the cooling status of the DC room, and a policy network is trained to predict optimized control settings when gave the current load and weather information. The proposed algorithm is evaluated on the EnergyPlus simulation platform and on a real data trace collected from the National Super Computing Centre (NSCC) of Singapore. Our results show that the proposed CCA can achieve about 11% cooling cost saving on the simulation platform compared with a manually configured baseline control algorithm. In the trace-based study, we propose a de-underestimation validation mechanism as we cannot directly test the algorithm on a real DC. Even though with DUE the results are conservative, we can still achieve about 15% cooling energy saving on the NSCC data trace if we set the inlet temperature threshold at 26.6 degree Celsius.
Sleep deprivation amplifies reactivity of brain reward networks, biasing the appraisal of positive emotional experiences.
(2011)

Appropriate interpretation of pleasurable, rewarding experiences favors decisions that enhance survival. Conversely, dysfunctional affective brain processing can lead to life-threatening risk behaviors (e.g., addiction) and emotion imbalance (e.g., mood disorders). The state of sleep deprivation continues to be associated with maladaptive emotional regulation, leading to exaggerated neural and behavioral reactivity to negative, aversive experiences. However, such detrimental consequences are paradoxically aligned with the perplexing antidepressant benefit of sleep deprivation, elevating mood in a proportion of patients with major depression. Nevertheless, it remains unknown how sleep loss alters the dynamics of brain and behavioral reactivity to rewarding, positive emotional experiences. Using functional magnetic resonance imaging (fMRI), here we demonstrate that sleep deprivation amplifies reactivity throughout human mesolimbic reward brain networks in response to pleasure-evoking stimuli. In addition, this amplified reactivity was associated with enhanced connectivity in early primary visual processing pathways and extended limbic regions, yet with a reduction in coupling with medial frontal and orbitofrontal regions. These neural changes were accompanied by a biased increase in the number of emotional stimuli judged as pleasant in the sleep-deprived group, the extent of which exclusively correlated with activity in mesolimbic regions. Together, these data support a view that sleep deprivation not only is associated with enhanced reactivity toward negative stimuli, but imposes a bidirectional nature of affective imbalance, associated with amplified reward-relevant reactivity toward pleasure-evoking stimuli also. Such findings may offer a neural foundation on which to consider interactions between sleep loss and emotional reactivity in a variety of clinical mood disorders.
Factors contributing to attrition behavior in diabetes self-management programs: A mixed method approach
(2008)

BACKGROUND
Diabetes self-management education is a critical component in diabetes care. Despite worldwide efforts to develop efficacious DSME programs, high attrition rates are often reported in clinical practice. The objective of this study was to examine factors that may contribute to attrition behavior in diabetes self-management programs.


METHODS
We conducted telephone interviews with individuals who had Type 2 diabetes (n = 267) and attended a diabetes education centre. Multivariable logistic regression was performed to identify factors associated with attrition behavior. Forty-four percent of participants (n = 118) withdrew prematurely from the program and were asked an open-ended question regarding their discontinuation of services. We used content analysis to code and generate themes, which were then organized under the Behavioral Model of Health Service Utilization.


RESULTS
Working full and part-time, being over 65 years of age, having a regular primary care physician or fewer diabetes symptoms were contributing factors to attrition behaviour in our multivariable logistic regression. The most common reasons given by participants for attrition from the program were conflict between their work schedules and the centre's hours of operation, patients' confidence in their own knowledge and ability when managing their diabetes, apathy towards diabetes education, distance to the centre, forgetfulness, regular physician consultation, low perceived seriousness of diabetes, and lack of familiarity with the centre and its services. There was considerable overlap between our quantitative and qualitative results.


CONCLUSION
Reducing attrition behaviour requires a range of strategies targeted towards delivering convenient and accessible services, familiarizing individuals with these services, increasing communication between centres and their patients, and creating better partnerships between centres and primary care physicians.
Bit-Cell Level Optimization for Non-volatile Memories Using Magnetic Tunnel Junctions and Spin-Transfer Torque Switching
(2012)

Spin-transfer torque magnetic random access memories (STT-MRAM), using magnetic tunnel junctions (MTJ), is a resistive memory technology that has spurred significant research interest due to its potential for on-chip, high-density, high-speed, low-power, and non-volatile memory. However, due to conflicting read and write requirements, there is a need to develop optimization techniques for designing STT-MRAM bit-cells to minimize read and write failures. We propose an optimization technique that minimizes read and write failures by proper selection of bit-cell configuration and by proper access transistor sizing. A mixed-mode simulation framework was developed to evaluate the effectiveness of our optimization technique. Our simulation framework captures the transport physics in the MTJ using Non-Equilibrium Green's Function method and self-consistently solves the MTJ magnetization dynamics using Landau-Lifshitz-Gilbert equation augmented with the full Slonczewski spin-torque term. The electrical parameters of the MTJ are then encapsulated in a Verilog-A model and used in HSPICE to perform bit-cell level optimization. The optimization technique is applied to STT-MRAM bit-cells designed using 45 nm bulk and 45 nm silicon-on-insulator CMOS technologies. Finally, predictions are made for optimized STT-MRAM bit-cells designed in 16 nm predictive technology.
State of health monitoring of Li-ion batteries using dynamic resistance mapping and regression
(2016)

The increase in speed and accuracy of computation methods and the advancement in sensor technologies has shifted the focus towards health monitoring systems of vehicles. Li-ion batteries which are used in electric vehicles, tend to get damaged and pose potential safety threats when ill-used. An adaptive control system called battery management system is needed to ensure the safety and life optimization of batteries in electric vehicles. This system takes care of both diagnosis and prognosis, giving the user an idea of current status as well as remaining lifetime of the battery. A novel algorithm based on dynamic resistance mapping is used to develop a data driven computational model for estimating the state of health of battery. A model with a correlation co-efficient of 99.45% is developed for state of health estimation to find the health of the battery during run time.
Metamorphic Testing: A Review of Challenges and Opportunities
(2018)

Metamorphic testing is an approach to both test case generation and test result verification. A central element is a set of metamorphic relations, which are necessary properties of the target function or algorithm in relation to multiple inputs and their expected outputs. Since its first publication, we have witnessed a rapidly increasing body of work examining metamorphic testing from various perspectives, including metamorphic relation identification, test case generation, integration with other software engineering techniques, and the validation and evaluation of software systems. In this article, we review the current research of metamorphic testing and discuss the challenges yet to be addressed. We also present visions for further improvement of metamorphic testing and highlight opportunities for new research.
Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis
(2010)

Nowadays because of the complicated nature of making decision in stock market and making real-time strategy for buying and selling stock via portfolio selection and maintenance, many research papers has involved stock price prediction issue. Low accuracy resulted by models may increase trade cost such as commission cost in more sequenced buy and sell signals because of insignificant alarms and otherwise bad diagnosis in price trend do not satisfy trader’s expectation and may involved him/her in irrecoverable cost. Therefore, in this paper, Neuro-Fuzzy Inference System adopted on a Takagi–Sugeno–Kang (TSK) type Fuzzy Rule Based System is developed for stock price prediction. The TSK fuzzy model applies the technical index as the input variables and the consequent part is a linear combination of the input variables. Fuzzy C-Mean clustering implemented for identifying number of rules. Initial membership function of the premise part approximately defined as Gaussian function. TSK parameters tuned by Adaptive NeroFuzzy Inference System (ANFIS). Proposed model is tested on the Tehran Stock Exchange Indexes (TEPIX). This index with high accuracy near by 97.8% has successfully forecasted with several experimental tests from different sectors. 2009 Published by Elsevier Ltd.
SIGNet: Scalable Embeddings for Signed Networks
(2018)

Recent successes in word embedding and document embedding have motivated researchers to explore similar representations for networks and to use such representations for tasks such as edge prediction, node label prediction, and community detection. Existing methods are largely focused on finding distributed representations for unsigned networks and are unable to discover embeddings that respect polarities inherent in edges. We propose SIGNet, a fast scalable embedding method suitable for signed networks. Our proposed objective function aims to carefully model the social structure implicit in signed networks by reinforcing the principles of social balance theory. Our method builds upon the traditional word2vec family of embedding approaches but we propose a new targeted node sampling strategy to maintain structural balance in higher-order neighborhoods. We demonstrate the superiority of SIGNet over state-of-the-art methods proposed for both signed and unsigned networks on several real world datasets from different domains. In particular, SIGNet offers an approach to generate a richer vocabulary of features of signed networks to support representation and reasoning.
Sliding mode control strategy for three-phase DVR employing twelve-switch voltage source converter
(2015)

In this study, a new method is proposed for the suppression of voltage amplitude fluctuations on the load terminals. Moreover, the voltage harmonics on the load terminals are effectively reduced below the defined limits in the IEEE-519 standard. In the studied topology, a twelve-switch three-phase voltage source converter is used for effective control on the zero-sequence components due to the unbalanced voltages or single phase voltage sags. In order to obtain a fast dynamic response, sliding mode control strategy is used to control the system. The existence condition of the sliding mode is presented. The performance of the closed-loop system is verified with cases of voltage sag and utility voltage harmonics. The overall system is explained in detail and verified by using MATLAB/Simulink.
Employing Aesthetic Principles for Automatic Photo Book Layout
(2011)

Photos are a common way of preserving our personal memories. The visual souvenir of a personal event is often composed into a photo collage or the pages of a photo album. Today we find many tools to help users creating such compositions by different tools for authoring photo compositions. Some template-based approaches generate nice presentations, however, come mostly with limited design variations. Creating complex and fancy designs for, e.g., a personal photo book, still demands design and composition skills to achieve results that are really pleasing to the eye – skills which many users simply lack. Professional designers instead would follow general design principles such as spatial layout rules, symmetry, balance among the element as well color schemes and harmony. In this paper, we propose an approach to deliver principles of design and composition to the end user by embedding it into an automatic composition application. We identify and analyze common design and composition principles and transfer these to the automatic creation of pleasant photo compositions by employing genetic algorithms. In contrast to other approaches, we strictly base our design system on common design principles, consider additional media types besides text in the photo book and specifically take the content of photos into account. Our approach is both implemented in a web-based rich media application and a tool for the automatic transformation from blogs into photo books.
Textual Spatial Cosine Similarity
(2015)

When dealing with document similarity many methods exist today, like cosine similarity. More complex methods are also available based on the semantic analysis of textual information, which are computationally expensive and rarely used in the real time feeding of content as in enterprisewide search environments. To address these real-time constraints, we developed a new measure of document similarity called Textual Spatial Cosine Similarity, which is able to detect similitude at the semantic level using word placement information contained in the document. We will see in this paper that two degenerate cases exist for this model, which coincide with Cosine Similarity on one side and with a paraphrasing detection model to the other.
SVM Based Effective Malware Detection System
(2015)

Malware is coined as an instance of malicious code that has the potential to harm a computer or network. Recent years have encountered massive growth in malwares as existing signature based malware detection approaches are becoming ineffective and intractable. Cyber criminals and malware developers have adapted code obfuscation techniques which undermines the effectiveness of malware defense mechanism. Hence we propounded a system which focuses on static analysis in addition with automated behavior analysis in emulated environment generating behavior reports to investigate malwares. The proposed method uses programs as opcode density histograms and reduces the explosion of features. We employed eigen vector subspace analysis to filter and diminish the misclassification and interference of features. Our system uses a hybrid approach for discovering malware based on support vector machine classifier so that potential of malware detection system can be leveraged to combat with diverse forms of malwares while attaining high accuracy and low false alarms. Keywords—Behavior Analysis, Static Analysis, Opcode Extraction, Malware Detection, Support Vector Machine.
Trends in ship electric propulsion
(2002)

Since the 1980s there has been an explosion in the number and variety of electric propulsion ships being built around the world with everything from cruise liners to amphibious assault ships adopting electric propulsion. This technology revolution has occurred, largely un-noticed outside the marine industry. This paper briefly describes what has transpired in recent decades, why the resurgence is occurring and what the future may hold for electrically propelled ships.
An Analysis of Mobile Banking User Behavior Using Customer Segmentation
(2015)

This study proposed an integrated data mining and customer behavior scoring model to manage existing mobile banking users in an Iranian bank. This segmentation model was developed to identify groups of customers based on transaction history, recency, frequency, monetary background. It classified mobile banking users into six groups. This study demonstrated that identifying customers by a behavioral scoring facilitates marketing strategy assignment. Then the bank can develop its marketing actions. Thus, the bank can attract more customers, maintain its customers, and keep high customers' satisfaction. Keywords—Data mining; mobile data, mobile banking; customer segmentation Introduction Online banking is increasingly common. Financial institutions deliver online services via various electronic channels, subsequently diminishing the importance of conventional branch networks. The newly emerging channels of online banking and rapidly increasing penetration rates of mobile phones motivate this study (C. S. Chen, 2013). The internet has had a significant impact on financial institutions, allowing consumers to access many bank facilities 24 hours a day, while allowing banks to significantly cut their costs. Research has shown that online banking is the cheapest delivery channel for many banking services (KoenigLewis, Palmer, & Moll, 2010; Robinson, 2000). A number of studies have identified advantages to bank customers, including cost and time savings as well as spatial independence benefits (Koenig-Lewis et al., 2010). According to Gartner’s prediction of leading trends of 2012 in mobile applications, mobile commerce (m-commerce) remains the most important one. Gartner further forecasts that mobile devices will replace PCs as the main device to access the internet. As for the third quarter of 2012, IPSOS indicated that “The era of Multi-Screen has come, and smartphones account for the purchasing behavior of 65% of mobile device users.” According to that report, 66 percent of the smartphone holders in Taiwan access the internet via a smartphone at least once daily; approximately 57 percent of the customers perform mobile searches; and 40 percent of the customers shop via mobile phones (IPSOS, 2012). These statistics reflect vigorous growth in the scale of m-commerce. However, mobile banking remains in its infancy, and international adoption rates demonstrate the strong potential of m-commerce (FRB, 2012). Therefore, data mining for mobile banking is of priority concern for further developing mobile banking services (MBSs) (C.
MFCC and VQ voice recognition based ATM security for the visually disabled
(2017)

A biometrie based automatic teller machine which works with a two-tier security using voice and fingerprint recognition can help users which are visually challenged, allowing them to use the machine using only their biometric characteristics. An automated teller machine (ATM) requires a user to pass an identity test using their PIN before doing any financial transactions. The current method available for access control in ATM is based on cards and pins which increases the issues of unauthorized access on accounts via card skimming. It is eminently difficult to avoid another person from attaining and using someone else's card also regular smartcards can be lost, duplicated, stolen or falsified with accuracy. Another concern is the accessibility of ATM to differently abled people. These concerns can be overcome by using fingerprint recognition for authentication, as discussed from the researchers' previous study, and by adding up an additional voice recognition system feature as discussed on this paper. The four fingerprint sample patterns of an individual are completely separate and uncorrelated. The action of fingerprint recognition involves pre-processing, feature extraction and minutiae matching. Matching is done by comparing the user's fingerprint with the existing fingerprint database, images which were acquired at the time of opening an account in the bank account. Once the fingerprint of the user passes the authentication procedures of the system the user is now able to carry out further transactions using voice-based commands by speaking through a microphone. This model not only provides security but also accessibility to certain sections of the population like people with visual impairment and eye disabilities. The system uses biometric based user recognition. The authenticity of the account will be checked by the input of the user's fingerprint, this will then allow further transaction via voice recognition that implements a MFCC, DWT and VQ to continue. ATM users are identified by the lowest VQ distortion of each voice input. Out of 50 different legitimate user trials, 42 tries were identified while 8 legitimate user tries were denied of access in the system producing 84% accuracy.
Weakly Supervised Facial Analysis with Dense Hyper-Column Features
(2016)

Weakly supervised methods have recently become one of the most popular machine learning methods since they are able to be used on large-scale datasets without the critical requirement of richly annotated data. In this paper, we present a novel, self-taught, discriminative facial feature analysis approach in the weakly supervised framework. Our method can find regions which are discriminative across classes yet consistent within a class and can solve many face related problems. The proposed method first trains a deep face model with high discriminative capability to extract facial features. The hypercolumn features are then used to give pixel level representation for better classification performance along with discriminative region detection. In addition, calibration approaches are proposed to enable the system to deal with multi-class and mixed-class problems. The system is also able to detect multiple discriminative regions from one image. Our uniform method is able to achieve competitive results in various face analysis applications, such as occlusion detection, face recognition, gender classification, twins verification and facial attractiveness analysis.
Towards Good Practices for Image Retrieval Based on CNN Features
(2017)

Recent works have demonstrated that Convolutional Neural Networks (CNNs) achieve state-of-the-art results in several computer vision tasks. CNNs have also shown their ability to provide effective descriptors for image retrieval. In this paper, we focus on CNN feature extraction for instance-level image search. We started by studying in depth several methods proposed to improve the Regional Maximal Activation (RMAC) approach. Then, we selected some of these advances and introduced a new approach that combines multi-scale and multi-layer feature extraction with feature selection. We also propose an approach for local RMAC descriptor extraction based on class activation maps. Our parameter-free approach provides short descriptors and achieves state-of-the-art performance without the need of CNN finetuning or additional data in any way. In order to demonstrate the effectiveness of our approach, we conducted extensive experiments on four well known instance-level image retrieval benchmarks (the INRIA Holidays dataset, the University of Kentucky Benchmark, Oxford5k and Paris6k).